MFC-GAN: Class-imbalanced dataset classification using Multiple Fake Class Generative Adversarial Network

By Adamu Ali-Gombe, Eyad Elyan. 2019

176 citations recorded by the source corpus.

Abstract Class-imbalanced datasets are common across different domains such as health, banking, security and others. With such datasets, the learning algorithms are often biased toward the majority class-instances. Data augmentation is a common approach that aims at rebalancing a dataset by injecting more data samples of the minority class instances. In this paper, a new data augmentation approach is proposed using a Generative Adversarial Networks (GAN) to handle the class imbalance problem. Unlike common GAN models, which use a single fake class, the proposed method uses multiple fake classes to ensure a fine-grained generation and classification of the minority class instances. Moreover, the proposed GAN model is conditioned to generate minority class instances aiming at rebalancing the dataset. Extensive experiments were carried out using public datasets, where synthetic samples generated using our model were added to the imbalanced dataset, followed by performing classification using Convolutional Neural Network. Experiment results show that our model can generate diverse minority class instances, even in extreme cases where the number of minority class instances is relatively low. Additionally, superior performance of our model over other common augmentation and oversampling methods was achieved in terms of classification accuracy and quality of the generated samples.

Subfields: Accounting, Artificial Intelligence, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Generative Models

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Rohitash Chandra

Citations in the corpus

A review of machine learning in processing remote sensing data for mineral exploration (2021, background): “GANs are more recently becoming very popular for addressing the issue of class-imbalanced problems (Ali-Gombe and Elyan, 2019; Shamsolmoali et al., 2020), which are key challenges in geoscience and remote sensing.”
A Comprehensive Survey of Image Augmentation Techniques for Deep Learning (2022, background): “In addition, MFC-GAN [49] adopts multiple fake classes in the classification loss to stabilize the training.”
Surface Defect Detection Methods for Industrial Products: A Review (2021, methodology): “The identification of unbalanced samples [97,98] is another difficulty based on the deep learning method when used in the surface defect detection of industrial products.”